In 2026, AI will continue to be a major corporate initiative. But, according to the new Top IT Insights for 2026 report from Unisys, most companies aren’t planning huge, transformational AI integration this year. Instead, many are looking at much smaller implementations, which have quicker pathways to ROI and boost efficiency for existing processes.

“We are going to see more functional deployments of AI, a focus on quality rather than cost-cutting, and the emergence of AI applications that will deliver repeatable, ROI-driven results,” Unisys CEO Mike Thomson said in a release.

Unisys sees three major internal AI applications that companies are likely to develop this year: Enterprise knowledge assistants that index company content and can answer questions, code development agents and AI-powered service operations to help to resolve simpler IT, HR and finance issues. And it’s likely that companies will veer away from using basic, general LLMs in favor of more specific ones—using data that is more customized to their enterprise, industry and way of doing business. There’s less LLM wrangling in this kind of deployment, and companies can concentrate on improving the quality of their AI responses. This can help bring greater ROI through eliminating the need for a human to have to redo the work that AI is supposed to perform.

This also will not be the year of large-scale layoffs due to AI, Unisys predicts. Any company that was once anticipating making broad cuts to human staff, the report states, will learn that layoffs will make innovation more difficult. Instead, job roles are likely to shift more this year, replacing responsibilities that are now done with AI with different ones to further the company’s evolution. The only exception, Unisys writes, is entry-level coders, whose skills will likely not be needed as much.

With AI, going small actually helps companies prepare for larger and more ambitious AI projects in the future, the report states. Smaller projects take less of a financial and personnel investment. It’s also an easier lift for staff and resources to do something that improves what the company already does. But the process of developing smaller and more limited AI deployments brings real learning to the team, increasing their knowledge both of how to make AI platforms work, and what is possible given the data, governance and infrastructure at the company. It also addresses what the real value of AI can be to a company. And with this experience, the years ahead can bring much bolder transformations.

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Forbes contributor Karl Freund distills the major reason this........

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